An ER-MKKNN (enhanced random mixed kernel K nearest neighbors algorithm) was developed to meet the requirements of base station network traffic prediction in ultra-dense 5G/6G environments. A hybrid kernel function was formed by combining a radial basis function kernel with a white-noise kernel, thereby overcoming the trade-off between nonlinear relationship modeling and noise suppression that plagues single-kernel methods. Dual random subsampling of both samples and features, together with a randomized hyperparameter-interval strategy, was employed to bolster generalization stability in high-dimensional, sparse settings. A dynamic weight-allocation mechanism based on inversion of out-of-bag errors was introduced to improve robustness against abrupt traffic fluctuations. Finally, a multi-level parallel architecture was implemented to deliver a scalable prediction framework for ultra-dense network topologies. Experimental evaluations show that ER-MKKNN outperformed deep-learning models in root mean square error, mean absolute percentage error and mean absolute error, respectively, establishing a new technical pathway for intelligent network operations and maintenance.